title: A nonlinear heartbeat dynamics model approach for personalized emotion recognition.
abstract: Emotion recognition based on autonomic nervous system signs is one of the ambitious goals of affective computing. It is well-accepted that standard signal processing techniques require relative long-time series of multivariate records to ensure reliability and robustness of recognition and classification algorithms. In this work, we present a novel methodology able to assess cardiovascular dynamics during short-time (i.e. < 10 seconds) affective stimuli, thus overcoming some of the limitations of current emotion recognition approaches. We developed a personalized, fully parametric probabilistic framework based on point-process theory where heartbeat events are modelled using a 2(nd)-order nonlinear autoregressive integrative structure in order to achieve effective performances in short-time affective assessment. Experimental results show a comprehensive emotional characterization of 4 subjects undergoing a passive affective elicitation using a sequence of standardized images gathered from the international affective picture system. Each picture was identified by the IAPS arousal and valence scores as well as by a self-reported emotional label associating a subjective positive or negative emotion. Results show a clear classification of two defined levels of arousal, valence and self-emotional state using features coming from the instantaneous spectrum and bispectrum of the considered RR intervals, reaching up to 90% recognition accuracy.
authors.forename: Gaetano
authors.initials: G
authors.lastname: Valenza
authors.forename: Luca
authors.initials: L
authors.lastname: Citi
authors.forename: Antonio
authors.initials: A
authors.lastname: Lanatà
authors.forename: Enzo Pasquale
authors.initials: EP
authors.lastname: Scilingo
authors.forename: Riccardo
authors.initials: R
authors.lastname: Barbieri
mesh_terms: D000465:Algorithms; D004644:Emotions / Q000502:physiology*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D017711:Nonlinear Dynamics; D046709:Pattern Recognition, Physiological; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult